Premature Ventricular Complexes: Assessing Burden Density in a Large National Cohort to Better Define Optimal ECG Monitoring Duration

BACKGROUND

Premature ventricular contractions (PVCs) burden is a risk factor for heart failure and cardiovascular death in patients with structural heart disease. Long-term ECG monitoring can have a significant impact on PVC burden evaluation by further defining PVC distribution patterns.

OBJECTIVE

This study aimed to ascertain the optimal duration of ECG monitoring to characterize PVC burden and understand clinical characteristics associated with frequent PVCs and NSVT in a large US cohort.

METHODS

Commercial data (iRhythm's Zio patch) from June 2011 to April 2022 were analyzed. Inclusion criteria were age >18 years, PVC burden ≥5%, and wear period ≥13 days. PVC burden cutoffs were determined based on AHA/ACC/HRS guidelines for very frequent PVCs (10,000-20,000 over 24 hours). Patients were categorized by PVC densities : Low (<10%), Moderate (10% to <20%) and High (≥20%). Mean measured error was assessed at baseline and daily until wear period's end for overall PVC Burden and different PVC densities.

RESULTS

Analysis of 106,705 patch monitors revealed a study population with mean age of 70.6±14.6 years; 33.6% female. PVC burden was higher in males and those >65 years of age. PVC burden mean error decreased from 2.9% at 24 hours to 1.3% at 7 days, and 0.7% at 10 days. Number of VT episodes per patient increased with increasing PVC burden (p<0.0001).

CONCLUSION

Extending ambulatory monitoring beyond 24 hours to 7 days or more, improves accuracy of assessing PVC burden. VT frequency and duration vary based on initial PVC density, highlighting the need for prolonged cardiac monitoring.

Branched Latent Neural Maps

We introduce Branched Latent Neural Maps (BLNMs) to learn finite dimensional input-output maps encoding complex physical processes. A BLNM is defined by a simple and compact feedforward partially-connected neural network that structurally disentangles inputs with different intrinsic roles, such as the time variable from model parameters of a differential equation, while transferring them into a generic field of interest. BLNMs leverage latent outputs to enhance the learned dynamics and break the curse of dimensionality by showing excellent in-distribution generalization properties with small training datasets and short training times on a single processor. Indeed, their in-distribution generalization error remains comparable regardless of the adopted discretization during the testing phase. Moreover, the partial connections, in place of a fully-connected structure, significantly reduce the number of tunable parameters. We show the capabilities of BLNMs in a challenging test case involving biophysically detailed electrophysiology simulations in a biventricular cardiac model of a pediatric patient with hypoplastic left heart syndrome. The model includes a 1D Purkinje network for fast conduction and a 3D heart-torso geometry. Specifically, we trained BLNMs on 150 in silico generated 12-lead electrocardiograms (ECGs) while spanning 7 model parameters, covering cell-scale, organ-level and electrical dyssynchrony. Although the 12-lead ECGs manifest very fast dynamics with sharp gradients, after automatic hyperparameter tuning the optimal BLNM, trained in less than 3 hours on a single CPU, retains just 7 hidden layers and 19 neurons per layer. The resulting mean square error is on the order of 10 - 4 on an independent test dataset comprised of 50 additional electrophysiology simulations. In the online phase, the BLNM allows for 5000x faster real-time simulations of cardiac electrophysiology on a single core standard computer and can be employed to solve inverse problems via global optimization in a few seconds of computational time. This paper provides a novel computational tool to build reliable and efficient reduced-order models for digital twinning in engineering applications. The Julia implementation is publicly available under MIT License at https://github.com/StanfordCBCL/BLNM.jl.

Radiotherapy-induced diffuse myocardial fibrosis in early-stage breast cancer patients - multimodality imaging study with six-year follow-up

BACKGROUND

Breast radiotherapy (RT) induces diffuse myocardial changes, which may increase the incidence of heart failure with preserved ejection fraction. This study aimed to evaluate the early signs of diffuse fibrosis after RT and their evolution during a six-year follow-up.

METHODS

Thirty patients with early-stage left-sided breast cancer were studied with echocardiography and electrocardiography (ECG) at baseline, after RT, and at three-year and six-year follow-up visits. Echocardiography analysis included an off-line analysis of integrated backscatter (IBS). ECG was analysed for fragmented QRS (fQRS). In addition, cardiac magnetic resonance (CMR) imaging was performed at the six-year control. The left ventricle 16-segment model was used in cardiac imaging, and respective local radiation doses were analysed.

RESULTS

Regional myocardial reflectivity in inferoseptal segments increased by 2.02 (4.53) dB (p = 0.026) and the percentage of leads with fQRS increased from 9.2 to 16.4% (p = 0.002) during the follow-up. In CMR imaging, abnormal extracellular volume (ECV) and T1 mapping values were found with anteroseptal and apical localization in a median of 3.5 (1.00-5.75) and 3 (1.25-4.00) segments, respectively. A higher left ventricle radiation dose was associated with an increased likelihood of having changes simultaneously in CMR and echocardiography (OR 1.26, 95% Cl. 1.00-1.59, p = 0.047).

CONCLUSIONS

After radiotherapy, progressive changes in markers of diffuse myocardial fibrosis were observed in a multimodal manner in ECG and echocardiography. Changes in echocardiography and abnormal values in CMR were localized in the septal and apical regions, and multiple changes were associated with higher radiation doses.

The conduction velocity-potassium relationship in the heart is modulated by sodium and calcium

The relationship between cardiac conduction velocity (CV) and extracellular potassium (K⁺) is biphasic, with modest hyperkalemia increasing CV and severe hyperkalemia slowing CV. Recent studies from our group suggest that elevating extracellular sodium (Na⁺) and calcium (Ca²⁺) can enhance CV by an extracellular pathway parallel to gap junctional coupling (GJC) called ephaptic coupling that can occur in the gap junction adjacent perinexus. However, it remains unknown whether these same interventions modulate CV as a function of K⁺. We hypothesize that Na⁺, Ca²⁺, and GJC can attenuate conduction slowing consequent to severe hyperkalemia. Elevating Ca²⁺ from 1.25 to 2.00 mM significantly narrowed perinexal width measured by transmission electron microscopy. Optically mapped, Langendorff-perfused guinea pig hearts perfused with increasing K⁺ revealed the expected biphasic CV-K⁺ relationship during perfusion with different Na⁺ and Ca²⁺ concentrations. Neither elevating Na⁺ nor Ca²⁺ alone consistently modulated the positive slope of CV-K⁺ or conduction slowing at 10-mM K⁺; however, combined Na⁺ and Ca²⁺ elevation significantly mitigated conduction slowing at 10-mM K⁺. Pharmacologic GJC inhibition with 30-μM carbenoxolone slowed CV without changing the shape of CV-K⁺ curves. A computational model of CV predicted that elevating Na⁺ and narrowing clefts between myocytes, as occur with perinexal narrowing, reduces the positive and negative slopes of the CV-K⁺ relationship but do not support a primary role of GJC or sodium channel conductance. These data demonstrate that combinatorial effects of Na⁺ and Ca²⁺ differentially modulate conduction during hyperkalemia, and enhancing determinants of ephaptic coupling may attenuate conduction changes in a variety of physiologic conditions.

Development and Human Factors Considerations for Extended Reality Applications in Medicine: The Enhanced ELectrophysiology Visualization and Interaction System (ĒLVIS)

With the rapid expansion of hardware options in the extended realities (XRs), there has been widespread development of applications throughout many fields, including engineering, entertainment and medicine. Development of medical applications for the XRs have a unique set of considerations during development and human factors testing. Additionally, understanding the constraints of the user and the use case allow for iterative improvement. In this manuscript, the authors discuss the considerations when developing and performing human factors testing for XR applications, using the Enhanced ELectrophysiology Visualization and Interaction System (ĒLVIS) as an example. Additionally, usability and critical interpersonal interaction data from first-in-human testing of ĒLVIS are presented.

Modeling Current Density Maps Using Aliev-Panfilov Electrophysiological Heart Model

Most existing studies of cardiac arrhythmia rely on surface measurements through optical or electrical mapping techniques. Current density imaging (CDI) is a method which enables us to study current pathways inside the tissue. However, this method entails implementation complexities for beating ex vivo hearts. Hence, this work presents an approach to simulate and study the current distributions in different cardiac electrophysiological states. The results are corroborated by experimental data, and they indicate that different states were distinguishable. The CDI simulations can be used for studying cardiac arrhythmias under simulation conditions which are otherwise impossible or difficult to be implemented experimentally.